Authored: Kathy N. Lam

Experiment: January 13, 2020

Updated: April 15, 2021

Set up

library(flowCore) #for reading and manipulating flow data
library(ggcyto) #for using ggplot with flow data
library(scales) #for nice plot axes
library(cowplot) #for multi panel plots
library(Phenoflow) #for rarefying
library(tidyverse) #for data wrangling and plotting

Read data

(fs = read.flowSet(path="data_fcs"))
## A flowSet with 100 experiments.
## 
##   column names:
##   530/30 Blue B-A 610/20 YG C-A FSC-A FSC-H FSC-W SSC-A SSC-H SSC-W Time
colnames(fs) 
## [1] "530/30 Blue B-A" "610/20 YG C-A"   "FSC-A"           "FSC-H"          
## [5] "FSC-W"           "SSC-A"           "SSC-H"           "SSC-W"          
## [9] "Time"
pData(phenoData(fs))
#read in sample info
metadata = read_tsv("metadata.tsv") %>%
    rename(name=Filename) %>%
    mutate(Mouse = paste("Mouse", Mouse)) %>%
    mutate(Day = paste("Day", Timepoint)) %>%
    mutate(Treatment_Mouse = paste0(Treatment, "\n", Mouse)) %>%
    mutate(Mouse_Treatment = paste0(Mouse, " (", Treatment, ")"))
## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   FlowSampleNumber = col_double(),
##   `Mouse Sample Number` = col_double(),
##   Experiment = col_character(),
##   Treatment = col_character(),
##   Mouse = col_double(),
##   Timepoint = col_double(),
##   Processing = col_character(),
##   CollectEvents = col_character(),
##   Filename = col_character(),
##   Notes = col_logical()
## )
metadata$Mouse = factor(metadata$Mouse, levels=unique(metadata$Mouse))
metadata$Treatment_Mouse = factor(metadata$Treatment_Mouse, levels=unique(metadata$Treatment_Mouse))
metadata$Treatment = factor(metadata$Treatment, levels=unique(metadata $Treatment))
metadata$Day = factor(metadata$Day, levels=unique(metadata$Day))
metadata
#add columns to phenoData  
phenoData(fs)$Order = seq(1, length(phenoData(fs)$name))
phenoData(fs)$Treatment = metadata$Treatment
phenoData(fs)$Timepoint = metadata$Timepoint
phenoData(fs)$Day = metadata$Day
phenoData(fs)$Name = metadata$name
phenoData(fs)$Mouse = metadata$Mouse
phenoData(fs)$Treatment_Mouse = metadata$Treatment_Mouse
phenoData(fs)$Mouse_Treatment = metadata$Mouse_Treatment
pData(phenoData(fs))
#make labeller function for facet_wrap
order = as.character(phenoData(fs)$Order)
name = phenoData(fs)$Name

order_names = mapply(c, order, name, SIMPLIFY = FALSE) #make a one-to-one 
order_names = lapply(order_names, `[[`, 2) #keep second element of each vector in the list
order_names = order_names[as.character(sort(as.numeric(names(order_names))))] #numerically sort 

order_labeller = function(variable,value){
  return(order_names[value])
}

Gate on scatter

scatter = rbind(c(0,   1e4), 
                c(1e5, 1e4),
                c(1e5, 2.5e5),  
                c(0,   2.5e5))
colnames(scatter)=c("FSC-A", "SSC-A")
scatter = as.data.frame(scatter)

ggplot() + 
    geom_point(data=fs, aes(x=`FSC-A`, y=`SSC-A`), shape=16, size=0.75, alpha=0.5) +
    scale_y_continuous(name="SSC-A (Granularity)\n", limits = c(-2e1,3e5)) +
    scale_x_continuous(name="\nFSC-A (Size)", limit=c(-2e1,3e5)) +
    facet_grid(Mouse~Timepoint) +
    theme_linedraw(14) +
    theme(panel.grid = element_blank(), axis.text.x=element_text(angle=90,hjust=1)) +
    geom_polygon(data=scatter, aes(x=`FSC-A`, y=`SSC-A`), fill=NA, colour="indianred", size=0.5,  linetype="solid") 
## Warning: Removed 29191 rows containing missing values (geom_point).


gate_scatter = polygonGate(filterId="scatter", `FSC-A` = scatter$`FSC-A`, `SSC-A` = scatter$`SSC-A`) 
result = flowCore::filter(fs, gate_scatter)
events = flowCore::Subset(fs, result)

Rarefy

events = Phenoflow::FCS_resample(events, replace = FALSE)
## Your samples range between 57249 and 90877 cells
## Your samples were randomly subsampled to 57249 cells

Gate on fluorescence

red = rbind(
    c(-1e3,   4e3),
    c(-1e3,   4e5),
    c( 4000,  4e5),
    c( 1000,  4e3))
colnames(red)=c("530/30 Blue B-A", "610/20 YG C-A")
red = as.data.frame(red)

green = rbind(
    c(9e2,   -1e3),
    c(4.5e3,  800),
    c(1.5e5,    800),
    c(1.5e5,   -1e3))
colnames(green)=c("530/30 Blue B-A", "610/20 YG C-A")
green = as.data.frame(green)

ggplot() + 
    geom_polygon(data=green, aes(x=`530/30 Blue B-A`, y=`610/20 YG C-A`), fill=NA, colour="green4", size=0.5, linetype="solid") +
    geom_polygon(data=red, aes(x=`530/30 Blue B-A`, y=`610/20 YG C-A`), fill=NA, colour="red3", size=0.5, linetype="solid") +
    geom_point(data=events, aes(x=`530/30 Blue B-A`, y=`610/20 YG C-A`), shape=16, size=0.5, alpha=0.2) +
    theme_linedraw(12) +
    ggcyto::scale_y_flowJo_biexp(name="mCherry intensity", widthBasis=-500, pos=5, neg=0, breaks=c(0, 1e3,1e4,1e5), limits=c(-1e3, 4e5)) +
    ggcyto::scale_x_flowJo_biexp(name="GFP intensity", widthBasis=-500, pos=5, neg=0, breaks=c(0, 1e3,1e4,1e5), limits=c(-1e3, 1.5e5)) +
    theme(panel.grid = element_blank(), 
          axis.title.x = element_text(colour="green4"), 
          axis.title.y = element_text(colour="red3")) +
    facet_grid(Mouse~Timepoint)  
## Warning: Removed 53 rows containing missing values (geom_point).

gate_green = polygonGate(filterId="green", `530/30 Blue B-A` = green$`530/30 Blue B-A`, `610/20 YG C-A` = green$`610/20 YG C-A`)
result_green = flowCore::filter(events, gate_green)
events_green = Subset(events, result_green)
gate_red = polygonGate(filterId="red", `530/30 Blue B-A` = red$`530/30 Blue B-A`, `610/20 YG C-A` = red$`610/20 YG C-A`)
result_red = flowCore::filter(events, gate_red)
events_red = Subset(events, result_red)
percent_red = toTable(summary(result_red)) %>% 
    mutate(x=2e5, y=2e5, colour="red", order=phenoData(events)$order) %>%
    rename(name=sample) %>%
    left_join(metadata, by="name")
## filter summary for frame 'Specimen_001_Sample_001_001.fcs'
##  red+: 5 of 57249 events (0.01%)
## 
## filter summary for frame 'Specimen_001_Sample_002_002.fcs'
##  red+: 2087 of 57249 events (3.65%)
## 
## filter summary for frame 'Specimen_001_Sample_003_003.fcs'
##  red+: 3443 of 57249 events (6.01%)
## 
## filter summary for frame 'Specimen_001_Sample_004_004.fcs'
##  red+: 840 of 57249 events (1.47%)
## 
## filter summary for frame 'Specimen_001_Sample_005_005.fcs'
##  red+: 87 of 57249 events (0.15%)
## 
## filter summary for frame 'Specimen_001_Sample_006_006.fcs'
##  red+: 2 of 57249 events (0.00%)
## 
## filter summary for frame 'Specimen_001_Sample_007_007.fcs'
##  red+: 6132 of 57249 events (10.71%)
## 
## filter summary for frame 'Specimen_001_Sample_008_008.fcs'
##  red+: 2294 of 57249 events (4.01%)
## 
## filter summary for frame 'Specimen_001_Sample_009_009.fcs'
##  red+: 24591 of 57249 events (42.95%)
## 
## filter summary for frame 'Specimen_001_Sample_010_010.fcs'
##  red+: 15153 of 57249 events (26.47%)
## 
## filter summary for frame 'Specimen_001_Sample_011_011.fcs'
##  red+: 11 of 57249 events (0.02%)
## 
## filter summary for frame 'Specimen_001_Sample_012_012.fcs'
##  red+: 3756 of 57249 events (6.56%)
## 
## filter summary for frame 'Specimen_001_Sample_013_013.fcs'
##  red+: 1314 of 57249 events (2.30%)
## 
## filter summary for frame 'Specimen_001_Sample_014_014.fcs'
##  red+: 473 of 57249 events (0.83%)
## 
## filter summary for frame 'Specimen_001_Sample_015_015.fcs'
##  red+: 72 of 57249 events (0.13%)
## 
## filter summary for frame 'Specimen_001_Sample_016_016.fcs'
##  red+: 0 of 57249 events (0.00%)
## 
## filter summary for frame 'Specimen_001_Sample_017_017.fcs'
##  red+: 3791 of 57249 events (6.62%)
## 
## filter summary for frame 'Specimen_001_Sample_018_018.fcs'
##  red+: 4 of 57249 events (0.01%)
## 
## filter summary for frame 'Specimen_001_Sample_019_019.fcs'
##  red+: 5 of 57249 events (0.01%)
## 
## filter summary for frame 'Specimen_001_Sample_020_020.fcs'
##  red+: 0 of 57249 events (0.00%)
## 
## filter summary for frame 'Specimen_001_Sample_021_021.fcs'
##  red+: 4 of 57249 events (0.01%)
## 
## filter summary for frame 'Specimen_001_Sample_022_022.fcs'
##  red+: 7909 of 57249 events (13.82%)
## 
## filter summary for frame 'Specimen_001_Sample_023_023.fcs'
##  red+: 357 of 57249 events (0.62%)
## 
## filter summary for frame 'Specimen_001_Sample_024_024.fcs'
##  red+: 47 of 57249 events (0.08%)
## 
## filter summary for frame 'Specimen_001_Sample_025_025.fcs'
##  red+: 2 of 57249 events (0.00%)
## 
## filter summary for frame 'Specimen_001_Sample_026_026.fcs'
##  red+: 30 of 57249 events (0.05%)
## 
## filter summary for frame 'Specimen_001_Sample_027_027.fcs'
##  red+: 3850 of 57249 events (6.73%)
## 
## filter summary for frame 'Specimen_001_Sample_028_028.fcs'
##  red+: 64 of 57249 events (0.11%)
## 
## filter summary for frame 'Specimen_001_Sample_029_029.fcs'
##  red+: 7 of 57249 events (0.01%)
## 
## filter summary for frame 'Specimen_001_Sample_030_030.fcs'
##  red+: 3 of 57249 events (0.01%)
## 
## filter summary for frame 'Specimen_001_Sample_031_031.fcs'
##  red+: 1 of 57249 events (0.00%)
## 
## filter summary for frame 'Specimen_001_Sample_032_032.fcs'
##  red+: 5355 of 57249 events (9.35%)
## 
## filter summary for frame 'Specimen_001_Sample_033_033.fcs'
##  red+: 10 of 57249 events (0.02%)
## 
## filter summary for frame 'Specimen_001_Sample_034_034.fcs'
##  red+: 4 of 57249 events (0.01%)
## 
## filter summary for frame 'Specimen_001_Sample_035_035.fcs'
##  red+: 24 of 57249 events (0.04%)
## 
## filter summary for frame 'Specimen_001_Sample_036_036.fcs'
##  red+: 0 of 57249 events (0.00%)
## 
## filter summary for frame 'Specimen_001_Sample_037_037.fcs'
##  red+: 10319 of 57249 events (18.02%)
## 
## filter summary for frame 'Specimen_001_Sample_038_038.fcs'
##  red+: 35 of 57249 events (0.06%)
## 
## filter summary for frame 'Specimen_001_Sample_039_039.fcs'
##  red+: 10 of 57249 events (0.02%)
## 
## filter summary for frame 'Specimen_001_Sample_040_040.fcs'
##  red+: 300 of 57249 events (0.52%)
## 
## filter summary for frame 'Specimen_001_Sample_041_041.fcs'
##  red+: 3 of 57249 events (0.01%)
## 
## filter summary for frame 'Specimen_001_Sample_042_042.fcs'
##  red+: 3808 of 57249 events (6.65%)
## 
## filter summary for frame 'Specimen_001_Sample_043_043.fcs'
##  red+: 18 of 57249 events (0.03%)
## 
## filter summary for frame 'Specimen_001_Sample_044_044.fcs'
##  red+: 45 of 57249 events (0.08%)
## 
## filter summary for frame 'Specimen_001_Sample_045_045.fcs'
##  red+: 2179 of 57249 events (3.81%)
## 
## filter summary for frame 'Specimen_001_Sample_046_046.fcs'
##  red+: 0 of 57249 events (0.00%)
## 
## filter summary for frame 'Specimen_001_Sample_047_047.fcs'
##  red+: 3498 of 57249 events (6.11%)
## 
## filter summary for frame 'Specimen_001_Sample_048_048.fcs'
##  red+: 3014 of 57249 events (5.26%)
## 
## filter summary for frame 'Specimen_001_Sample_049_049.fcs'
##  red+: 12453 of 57249 events (21.75%)
## 
## filter summary for frame 'Specimen_001_Sample_050_050.fcs'
##  red+: 1546 of 57249 events (2.70%)
## 
## filter summary for frame 'Specimen_001_Sample_051_051.fcs'
##  red+: 0 of 57249 events (0.00%)
## 
## filter summary for frame 'Specimen_001_Sample_052_052.fcs'
##  red+: 2088 of 57249 events (3.65%)
## 
## filter summary for frame 'Specimen_001_Sample_053_053.fcs'
##  red+: 9870 of 57249 events (17.24%)
## 
## filter summary for frame 'Specimen_001_Sample_054_054.fcs'
##  red+: 7503 of 57249 events (13.11%)
## 
## filter summary for frame 'Specimen_001_Sample_055_055.fcs'
##  red+: 6183 of 57249 events (10.80%)
## 
## filter summary for frame 'Specimen_001_Sample_056_056.fcs'
##  red+: 6 of 57249 events (0.01%)
## 
## filter summary for frame 'Specimen_001_Sample_057_057.fcs'
##  red+: 5224 of 57249 events (9.13%)
## 
## filter summary for frame 'Specimen_001_Sample_058_058.fcs'
##  red+: 13316 of 57249 events (23.26%)
## 
## filter summary for frame 'Specimen_001_Sample_059_059.fcs'
##  red+: 4429 of 57249 events (7.74%)
## 
## filter summary for frame 'Specimen_001_Sample_060_060.fcs'
##  red+: 15904 of 57249 events (27.78%)
## 
## filter summary for frame 'Specimen_001_Sample_061_061.fcs'
##  red+: 3 of 57249 events (0.01%)
## 
## filter summary for frame 'Specimen_001_Sample_062_062.fcs'
##  red+: 2664 of 57249 events (4.65%)
## 
## filter summary for frame 'Specimen_001_Sample_063_063.fcs'
##  red+: 6 of 57249 events (0.01%)
## 
## filter summary for frame 'Specimen_001_Sample_064_064.fcs'
##  red+: 1 of 57249 events (0.00%)
## 
## filter summary for frame 'Specimen_001_Sample_065_065.fcs'
##  red+: 19318 of 57249 events (33.74%)
## 
## filter summary for frame 'Specimen_001_Sample_066_066.fcs'
##  red+: 1 of 57249 events (0.00%)
## 
## filter summary for frame 'Specimen_001_Sample_067_067.fcs'
##  red+: 5258 of 57249 events (9.18%)
## 
## filter summary for frame 'Specimen_001_Sample_068_068.fcs'
##  red+: 6 of 57249 events (0.01%)
## 
## filter summary for frame 'Specimen_001_Sample_069_069.fcs'
##  red+: 4 of 57249 events (0.01%)
## 
## filter summary for frame 'Specimen_001_Sample_070_070.fcs'
##  red+: 14 of 57249 events (0.02%)
## 
## filter summary for frame 'Specimen_001_Sample_071_071.fcs'
##  red+: 5 of 57249 events (0.01%)
## 
## filter summary for frame 'Specimen_001_Sample_072_072.fcs'
##  red+: 3385 of 57249 events (5.91%)
## 
## filter summary for frame 'Specimen_001_Sample_073_073.fcs'
##  red+: 7213 of 57249 events (12.60%)
## 
## filter summary for frame 'Specimen_001_Sample_074_074.fcs'
##  red+: 5397 of 57249 events (9.43%)
## 
## filter summary for frame 'Specimen_001_Sample_075_075.fcs'
##  red+: 1193 of 57249 events (2.08%)
## 
## filter summary for frame 'Specimen_001_Sample_076_076.fcs'
##  red+: 18 of 57249 events (0.03%)
## 
## filter summary for frame 'Specimen_001_Sample_077_077.fcs'
##  red+: 2661 of 57249 events (4.65%)
## 
## filter summary for frame 'Specimen_001_Sample_078_078.fcs'
##  red+: 17966 of 57249 events (31.38%)
## 
## filter summary for frame 'Specimen_001_Sample_079_079.fcs'
##  red+: 15955 of 57249 events (27.87%)
## 
## filter summary for frame 'Specimen_001_Sample_080_080.fcs'
##  red+: 7405 of 57249 events (12.93%)
## 
## filter summary for frame 'Specimen_001_Sample_081_081.fcs'
##  red+: 0 of 57249 events (0.00%)
## 
## filter summary for frame 'Specimen_001_Sample_082_082.fcs'
##  red+: 2256 of 57249 events (3.94%)
## 
## filter summary for frame 'Specimen_001_Sample_083_083.fcs'
##  red+: 35881 of 57249 events (62.68%)
## 
## filter summary for frame 'Specimen_001_Sample_084_084.fcs'
##  red+: 16437 of 57249 events (28.71%)
## 
## filter summary for frame 'Specimen_001_Sample_085_085.fcs'
##  red+: 1267 of 57249 events (2.21%)
## 
## filter summary for frame 'Specimen_001_Sample_086_086.fcs'
##  red+: 2 of 57249 events (0.00%)
## 
## filter summary for frame 'Specimen_001_Sample_087_087.fcs'
##  red+: 4509 of 57249 events (7.88%)
## 
## filter summary for frame 'Specimen_001_Sample_088_088.fcs'
##  red+: 10497 of 57249 events (18.34%)
## 
## filter summary for frame 'Specimen_001_Sample_089_089.fcs'
##  red+: 14524 of 57249 events (25.37%)
## 
## filter summary for frame 'Specimen_001_Sample_090_090.fcs'
##  red+: 9627 of 57249 events (16.82%)
## 
## filter summary for frame 'Specimen_001_Sample_091_091.fcs'
##  red+: 1 of 57249 events (0.00%)
## 
## filter summary for frame 'Specimen_001_Sample_092_092.fcs'
##  red+: 2920 of 57249 events (5.10%)
## 
## filter summary for frame 'Specimen_001_Sample_093_093.fcs'
##  red+: 7214 of 57249 events (12.60%)
## 
## filter summary for frame 'Specimen_001_Sample_094_094.fcs'
##  red+: 11812 of 57249 events (20.63%)
## 
## filter summary for frame 'Specimen_001_Sample_095_095.fcs'
##  red+: 10892 of 57249 events (19.03%)
## 
## filter summary for frame 'Specimen_001_Sample_096_096.fcs'
##  red+: 2 of 57249 events (0.00%)
## 
## filter summary for frame 'Specimen_001_Sample_097_097.fcs'
##  red+: 2201 of 57249 events (3.84%)
## 
## filter summary for frame 'Specimen_001_Sample_098_098.fcs'
##  red+: 10339 of 57249 events (18.06%)
## 
## filter summary for frame 'Specimen_001_Sample_099_099.fcs'
##  red+: 24609 of 57249 events (42.99%)
## 
## filter summary for frame 'Specimen_001_Sample_100_100.fcs'
##  red+: 1637 of 57249 events (2.86%)
percent_green = toTable(summary(result_green)) %>%
    mutate(x=2e5, y=1e5, colour="green", order=phenoData(events)$order) %>%
    rename(name=sample) %>%
    left_join(metadata, by="name")
## filter summary for frame 'Specimen_001_Sample_001_001.fcs'
##  green+: 172 of 57249 events (0.30%)
## 
## filter summary for frame 'Specimen_001_Sample_002_002.fcs'
##  green+: 4459 of 57249 events (7.79%)
## 
## filter summary for frame 'Specimen_001_Sample_003_003.fcs'
##  green+: 9219 of 57249 events (16.10%)
## 
## filter summary for frame 'Specimen_001_Sample_004_004.fcs'
##  green+: 15379 of 57249 events (26.86%)
## 
## filter summary for frame 'Specimen_001_Sample_005_005.fcs'
##  green+: 6685 of 57249 events (11.68%)
## 
## filter summary for frame 'Specimen_001_Sample_006_006.fcs'
##  green+: 113 of 57249 events (0.20%)
## 
## filter summary for frame 'Specimen_001_Sample_007_007.fcs'
##  green+: 7115 of 57249 events (12.43%)
## 
## filter summary for frame 'Specimen_001_Sample_008_008.fcs'
##  green+: 5511 of 57249 events (9.63%)
## 
## filter summary for frame 'Specimen_001_Sample_009_009.fcs'
##  green+: 1110 of 57249 events (1.94%)
## 
## filter summary for frame 'Specimen_001_Sample_010_010.fcs'
##  green+: 13314 of 57249 events (23.26%)
## 
## filter summary for frame 'Specimen_001_Sample_011_011.fcs'
##  green+: 375 of 57249 events (0.66%)
## 
## filter summary for frame 'Specimen_001_Sample_012_012.fcs'
##  green+: 6236 of 57249 events (10.89%)
## 
## filter summary for frame 'Specimen_001_Sample_013_013.fcs'
##  green+: 9119 of 57249 events (15.93%)
## 
## filter summary for frame 'Specimen_001_Sample_014_014.fcs'
##  green+: 19590 of 57249 events (34.22%)
## 
## filter summary for frame 'Specimen_001_Sample_015_015.fcs'
##  green+: 7029 of 57249 events (12.28%)
## 
## filter summary for frame 'Specimen_001_Sample_016_016.fcs'
##  green+: 196 of 57249 events (0.34%)
## 
## filter summary for frame 'Specimen_001_Sample_017_017.fcs'
##  green+: 9729 of 57249 events (16.99%)
## 
## filter summary for frame 'Specimen_001_Sample_018_018.fcs'
##  green+: 20725 of 57249 events (36.20%)
## 
## filter summary for frame 'Specimen_001_Sample_019_019.fcs'
##  green+: 12334 of 57249 events (21.54%)
## 
## filter summary for frame 'Specimen_001_Sample_020_020.fcs'
##  green+: 8292 of 57249 events (14.48%)
## 
## filter summary for frame 'Specimen_001_Sample_021_021.fcs'
##  green+: 160 of 57249 events (0.28%)
## 
## filter summary for frame 'Specimen_001_Sample_022_022.fcs'
##  green+: 19642 of 57249 events (34.31%)
## 
## filter summary for frame 'Specimen_001_Sample_023_023.fcs'
##  green+: 12239 of 57249 events (21.38%)
## 
## filter summary for frame 'Specimen_001_Sample_024_024.fcs'
##  green+: 19175 of 57249 events (33.49%)
## 
## filter summary for frame 'Specimen_001_Sample_025_025.fcs'
##  green+: 18765 of 57249 events (32.78%)
## 
## filter summary for frame 'Specimen_001_Sample_026_026.fcs'
##  green+: 564 of 57249 events (0.99%)
## 
## filter summary for frame 'Specimen_001_Sample_027_027.fcs'
##  green+: 5148 of 57249 events (8.99%)
## 
## filter summary for frame 'Specimen_001_Sample_028_028.fcs'
##  green+: 7925 of 57249 events (13.84%)
## 
## filter summary for frame 'Specimen_001_Sample_029_029.fcs'
##  green+: 9010 of 57249 events (15.74%)
## 
## filter summary for frame 'Specimen_001_Sample_030_030.fcs'
##  green+: 22145 of 57249 events (38.68%)
## 
## filter summary for frame 'Specimen_001_Sample_031_031.fcs'
##  green+: 72 of 57249 events (0.13%)
## 
## filter summary for frame 'Specimen_001_Sample_032_032.fcs'
##  green+: 6376 of 57249 events (11.14%)
## 
## filter summary for frame 'Specimen_001_Sample_033_033.fcs'
##  green+: 421 of 57249 events (0.74%)
## 
## filter summary for frame 'Specimen_001_Sample_034_034.fcs'
##  green+: 9851 of 57249 events (17.21%)
## 
## filter summary for frame 'Specimen_001_Sample_035_035.fcs'
##  green+: 18017 of 57249 events (31.47%)
## 
## filter summary for frame 'Specimen_001_Sample_036_036.fcs'
##  green+: 36 of 57249 events (0.06%)
## 
## filter summary for frame 'Specimen_001_Sample_037_037.fcs'
##  green+: 16007 of 57249 events (27.96%)
## 
## filter summary for frame 'Specimen_001_Sample_038_038.fcs'
##  green+: 425 of 57249 events (0.74%)
## 
## filter summary for frame 'Specimen_001_Sample_039_039.fcs'
##  green+: 296 of 57249 events (0.52%)
## 
## filter summary for frame 'Specimen_001_Sample_040_040.fcs'
##  green+: 1567 of 57249 events (2.74%)
## 
## filter summary for frame 'Specimen_001_Sample_041_041.fcs'
##  green+: 239 of 57249 events (0.42%)
## 
## filter summary for frame 'Specimen_001_Sample_042_042.fcs'
##  green+: 6328 of 57249 events (11.05%)
## 
## filter summary for frame 'Specimen_001_Sample_043_043.fcs'
##  green+: 326 of 57249 events (0.57%)
## 
## filter summary for frame 'Specimen_001_Sample_044_044.fcs'
##  green+: 233 of 57249 events (0.41%)
## 
## filter summary for frame 'Specimen_001_Sample_045_045.fcs'
##  green+: 6152 of 57249 events (10.75%)
## 
## filter summary for frame 'Specimen_001_Sample_046_046.fcs'
##  green+: 107 of 57249 events (0.19%)
## 
## filter summary for frame 'Specimen_001_Sample_047_047.fcs'
##  green+: 6647 of 57249 events (11.61%)
## 
## filter summary for frame 'Specimen_001_Sample_048_048.fcs'
##  green+: 12613 of 57249 events (22.03%)
## 
## filter summary for frame 'Specimen_001_Sample_049_049.fcs'
##  green+: 11226 of 57249 events (19.61%)
## 
## filter summary for frame 'Specimen_001_Sample_050_050.fcs'
##  green+: 335 of 57249 events (0.59%)
## 
## filter summary for frame 'Specimen_001_Sample_051_051.fcs'
##  green+: 66 of 57249 events (0.12%)
## 
## filter summary for frame 'Specimen_001_Sample_052_052.fcs'
##  green+: 3711 of 57249 events (6.48%)
## 
## filter summary for frame 'Specimen_001_Sample_053_053.fcs'
##  green+: 776 of 57249 events (1.36%)
## 
## filter summary for frame 'Specimen_001_Sample_054_054.fcs'
##  green+: 2662 of 57249 events (4.65%)
## 
## filter summary for frame 'Specimen_001_Sample_055_055.fcs'
##  green+: 468 of 57249 events (0.82%)
## 
## filter summary for frame 'Specimen_001_Sample_056_056.fcs'
##  green+: 400 of 57249 events (0.70%)
## 
## filter summary for frame 'Specimen_001_Sample_057_057.fcs'
##  green+: 12615 of 57249 events (22.04%)
## 
## filter summary for frame 'Specimen_001_Sample_058_058.fcs'
##  green+: 444 of 57249 events (0.78%)
## 
## filter summary for frame 'Specimen_001_Sample_059_059.fcs'
##  green+: 4125 of 57249 events (7.21%)
## 
## filter summary for frame 'Specimen_001_Sample_060_060.fcs'
##  green+: 459 of 57249 events (0.80%)
## 
## filter summary for frame 'Specimen_001_Sample_061_061.fcs'
##  green+: 274 of 57249 events (0.48%)
## 
## filter summary for frame 'Specimen_001_Sample_062_062.fcs'
##  green+: 4881 of 57249 events (8.53%)
## 
## filter summary for frame 'Specimen_001_Sample_063_063.fcs'
##  green+: 389 of 57249 events (0.68%)
## 
## filter summary for frame 'Specimen_001_Sample_064_064.fcs'
##  green+: 161 of 57249 events (0.28%)
## 
## filter summary for frame 'Specimen_001_Sample_065_065.fcs'
##  green+: 1039 of 57249 events (1.81%)
## 
## filter summary for frame 'Specimen_001_Sample_066_066.fcs'
##  green+: 92 of 57249 events (0.16%)
## 
## filter summary for frame 'Specimen_001_Sample_067_067.fcs'
##  green+: 9414 of 57249 events (16.44%)
## 
## filter summary for frame 'Specimen_001_Sample_068_068.fcs'
##  green+: 464 of 57249 events (0.81%)
## 
## filter summary for frame 'Specimen_001_Sample_069_069.fcs'
##  green+: 176 of 57249 events (0.31%)
## 
## filter summary for frame 'Specimen_001_Sample_070_070.fcs'
##  green+: 450 of 57249 events (0.79%)
## 
## filter summary for frame 'Specimen_001_Sample_071_071.fcs'
##  green+: 175 of 57249 events (0.31%)
## 
## filter summary for frame 'Specimen_001_Sample_072_072.fcs'
##  green+: 6557 of 57249 events (11.45%)
## 
## filter summary for frame 'Specimen_001_Sample_073_073.fcs'
##  green+: 608 of 57249 events (1.06%)
## 
## filter summary for frame 'Specimen_001_Sample_074_074.fcs'
##  green+: 341 of 57249 events (0.60%)
## 
## filter summary for frame 'Specimen_001_Sample_075_075.fcs'
##  green+: 34 of 57249 events (0.06%)
## 
## filter summary for frame 'Specimen_001_Sample_076_076.fcs'
##  green+: 434 of 57249 events (0.76%)
## 
## filter summary for frame 'Specimen_001_Sample_077_077.fcs'
##  green+: 4722 of 57249 events (8.25%)
## 
## filter summary for frame 'Specimen_001_Sample_078_078.fcs'
##  green+: 153 of 57249 events (0.27%)
## 
## filter summary for frame 'Specimen_001_Sample_079_079.fcs'
##  green+: 464 of 57249 events (0.81%)
## 
## filter summary for frame 'Specimen_001_Sample_080_080.fcs'
##  green+: 120 of 57249 events (0.21%)
## 
## filter summary for frame 'Specimen_001_Sample_081_081.fcs'
##  green+: 54 of 57249 events (0.09%)
## 
## filter summary for frame 'Specimen_001_Sample_082_082.fcs'
##  green+: 3920 of 57249 events (6.85%)
## 
## filter summary for frame 'Specimen_001_Sample_083_083.fcs'
##  green+: 31 of 57249 events (0.05%)
## 
## filter summary for frame 'Specimen_001_Sample_084_084.fcs'
##  green+: 185 of 57249 events (0.32%)
## 
## filter summary for frame 'Specimen_001_Sample_085_085.fcs'
##  green+: 301 of 57249 events (0.53%)
## 
## filter summary for frame 'Specimen_001_Sample_086_086.fcs'
##  green+: 91 of 57249 events (0.16%)
## 
## filter summary for frame 'Specimen_001_Sample_087_087.fcs'
##  green+: 7977 of 57249 events (13.93%)
## 
## filter summary for frame 'Specimen_001_Sample_088_088.fcs'
##  green+: 71 of 57249 events (0.12%)
## 
## filter summary for frame 'Specimen_001_Sample_089_089.fcs'
##  green+: 169 of 57249 events (0.30%)
## 
## filter summary for frame 'Specimen_001_Sample_090_090.fcs'
##  green+: 190 of 57249 events (0.33%)
## 
## filter summary for frame 'Specimen_001_Sample_091_091.fcs'
##  green+: 55 of 57249 events (0.10%)
## 
## filter summary for frame 'Specimen_001_Sample_092_092.fcs'
##  green+: 16714 of 57249 events (29.20%)
## 
## filter summary for frame 'Specimen_001_Sample_093_093.fcs'
##  green+: 281 of 57249 events (0.49%)
## 
## filter summary for frame 'Specimen_001_Sample_094_094.fcs'
##  green+: 1355 of 57249 events (2.37%)
## 
## filter summary for frame 'Specimen_001_Sample_095_095.fcs'
##  green+: 3542 of 57249 events (6.19%)
## 
## filter summary for frame 'Specimen_001_Sample_096_096.fcs'
##  green+: 142 of 57249 events (0.25%)
## 
## filter summary for frame 'Specimen_001_Sample_097_097.fcs'
##  green+: 5355 of 57249 events (9.35%)
## 
## filter summary for frame 'Specimen_001_Sample_098_098.fcs'
##  green+: 138 of 57249 events (0.24%)
## 
## filter summary for frame 'Specimen_001_Sample_099_099.fcs'
##  green+: 171 of 57249 events (0.30%)
## 
## filter summary for frame 'Specimen_001_Sample_100_100.fcs'
##  green+: 65 of 57249 events (0.11%)
percentage = bind_rows(percent_red, percent_green) %>%
    group_by(name) %>% 
    mutate(total_gfp_mcherry = sum(true)) %>%
    mutate(fraction_fluor = round(true/total_gfp_mcherry, 3)) %>%
    mutate(percent_fluor = fraction_fluor*100) %>%
    mutate(label = paste(true, "events"))
ggplot() + 
    geom_point(data=events, aes(x=`530/30 Blue B-A`, y=`610/20 YG C-A`), shape=16, size=0.5, alpha=0.2, colour="black") +
    geom_point(data=events_green, aes(x=`530/30 Blue B-A`, y=`610/20 YG C-A`), shape=16, size=0.5, alpha=0.2, colour="green4") +
    geom_point(data=events_red, aes(x=`530/30 Blue B-A`, y=`610/20 YG C-A`), shape=16, size=0.5, alpha=0.2, colour="red3") +
    geom_text(data=percentage, aes(x=x, y=y, label=label, colour=colour), size=3, hjust=1) +
    geom_text(data=percentage, aes(x=2e5, y=4e5, label=paste0(count, " events total")), colour="grey27", size=3, hjust=1) +
    scale_colour_manual(values=c("green4", "red3")) +
    ggcyto::scale_y_flowJo_biexp(name="mCherry intensity", widthBasis=-500, pos=5, neg=0, breaks=c(0, 1e3,1e4,1e5), limits=c(-1e3, 5.5e5)) +
    ggcyto::scale_x_flowJo_biexp(name="GFP intensity", widthBasis=-500, pos=5, neg=0, breaks=c(0, 1e3,1e4,1e5), limits=c(-1e3, 3e5)) +
    theme_linedraw(12) +
    theme(panel.grid = element_blank(), 
          axis.title.x = element_text(colour="green4"), 
          axis.title.y = element_text(colour="red3"), 
          legend.position = "none") +
    facet_grid(Mouse~Timepoint) 
## Warning: Removed 3 rows containing missing values (geom_point).

ggsave("figures/exp17_GFP_mCherry_scatterplot.png", width=12, height=40, dpi=150)
## Warning: Removed 3 rows containing missing values (geom_point).

Make plots split on NT / GFPT

samples = metadata %>%
    filter(Treatment == "NT") %>%
    pull(FlowSampleNumber)

percentage_samples = percentage %>%
    filter(FlowSampleNumber %in% samples)

ggplot() + 
    geom_point(data=events[samples], aes(x=`530/30 Blue B-A`, y=`610/20 YG C-A`), shape=16, size=0.5, alpha=0.2, colour="black") +
    geom_point(data=events_green[samples], aes(x=`530/30 Blue B-A`, y=`610/20 YG C-A`), shape=16, size=0.5, alpha=0.2, colour="green4") +
    geom_point(data=events_red[samples], aes(x=`530/30 Blue B-A`, y=`610/20 YG C-A`), shape=16, size=0.5, alpha=0.2, colour="red3") +
    geom_text(data=percentage_samples, aes(x=x, y=y, label=label, colour=colour), size=3, hjust=1) +
    geom_text(data=percentage_samples, aes(x=2e5, y=4e5, label=paste0(count, " events total")), colour="grey27", size=3, hjust=1) +
    scale_colour_manual(values=c("green4", "red3")) +
    ggcyto::scale_y_flowJo_biexp(name="mCherry intensity", widthBasis=-500, pos=5, neg=0, breaks=c(0, 1e3,1e4,1e5), limits=c(-1e3, 5.5e5)) +
    ggcyto::scale_x_flowJo_biexp(name="GFP intensity", widthBasis=-500, pos=5, neg=0, breaks=c(0, 1e3,1e4,1e5), limits=c(-1e3, 3e5)) +
    theme_linedraw(12) +
    theme(panel.grid = element_blank(), 
          axis.title.x = element_text(colour="green4"), 
          axis.title.y = element_text(colour="red3"), 
          legend.position = "none") +
    facet_grid(Mouse_Treatment~Day) 
## Warning: Removed 1 rows containing missing values (geom_point).

ggsave("figures/exp17_GFP_mCherry_scatterplot_NT.png", width=12, height=20, dpi=150)
## Warning: Removed 1 rows containing missing values (geom_point).
samples = metadata %>%
    filter(Treatment == "GFPT") %>%
    pull(FlowSampleNumber)

percentage_samples = percentage %>%
    filter(FlowSampleNumber %in% samples)

ggplot() + 
    geom_point(data=events[samples], aes(x=`530/30 Blue B-A`, y=`610/20 YG C-A`), shape=16, size=0.5, alpha=0.2, colour="black") +
    geom_point(data=events_green[samples], aes(x=`530/30 Blue B-A`, y=`610/20 YG C-A`), shape=16, size=0.5, alpha=0.2, colour="green4") +
    geom_point(data=events_red[samples], aes(x=`530/30 Blue B-A`, y=`610/20 YG C-A`), shape=16, size=0.5, alpha=0.2, colour="red3") +
    geom_text(data=percentage_samples, aes(x=x, y=y, label=label, colour=colour), size=3, hjust=1) +
    geom_text(data=percentage_samples, aes(x=2e5, y=4e5, label=paste0(count, " events total")), colour="grey27", size=3, hjust=1) +
    scale_colour_manual(values=c("green4", "red3")) +
    ggcyto::scale_y_flowJo_biexp(name="mCherry intensity", widthBasis=-500, pos=5, neg=0, breaks=c(0, 1e3,1e4,1e5), limits=c(-1e3, 5.5e5)) +
    ggcyto::scale_x_flowJo_biexp(name="GFP intensity", widthBasis=-500, pos=5, neg=0, breaks=c(0, 1e3,1e4,1e5), limits=c(-1e3, 3e5)) +
    theme_linedraw(12) +
    theme(panel.grid = element_blank(), 
          axis.title.x = element_text(colour="green4"), 
          axis.title.y = element_text(colour="red3"), 
          legend.position = "none") +
    facet_grid(Mouse_Treatment~Day) 
## Warning: Removed 2 rows containing missing values (geom_point).

ggsave("figures/exp17_GFP_mCherry_scatterplot_GFPT.png", width=12, height=20, dpi=150)
## Warning: Removed 2 rows containing missing values (geom_point).

Make plots for representative mice

subset = c("Mouse 2", "Mouse 15")

samples = metadata %>% 
    filter(Mouse %in% subset) %>%
    pull(FlowSampleNumber)

percentage_samples = percentage %>% 
    filter(Mouse %in% subset)

ggplot() + 
    geom_point(data=events[samples], aes(x=`530/30 Blue B-A`, y=`610/20 YG C-A`), shape=16, size=0.5, alpha=0.2, colour="black") +
    geom_point(data=events_green[samples], aes(x=`530/30 Blue B-A`, y=`610/20 YG C-A`), shape=16, size=0.5, alpha=0.2, colour="green4") +
    geom_point(data=events_red[samples], aes(x=`530/30 Blue B-A`, y=`610/20 YG C-A`), shape=16, size=0.5, alpha=0.2, colour="red3") +
    geom_text(data=percentage_samples, aes(x=x, y=y, label=label, colour=colour), size=3, hjust=1) +
    geom_text(data=percentage_samples, aes(x=2e5, y=4e5, label=paste0(count, " events total")), colour="grey27", size=3, hjust=1) +
    scale_colour_manual(values=c("green4", "red3")) +
    ggcyto::scale_y_flowJo_biexp(name="mCherry intensity", widthBasis=-500, pos=5, neg=0, breaks=c(0, 1e3,1e4,1e5), limits=c(-1e3, 5.5e5)) +
    ggcyto::scale_x_flowJo_biexp(name="GFP intensity", widthBasis=-500, pos=5, neg=0, breaks=c(0, 1e3,1e4,1e5), limits=c(-1e3, 3e5)) +
    theme_linedraw(14) +
    theme(panel.grid = element_blank(), 
          panel.border = element_rect(size=1.25),
          axis.title.x = element_text(colour="green4"), 
          axis.title.y = element_text(colour="red3"), 
          legend.position = "none") +
    facet_grid(Treatment_Mouse~Day) 

ggsave("figures/exp17_GFP_mCherry_scatterplot_M2_M15.png", width=12, height=5, dpi=150)

Calculate and subtract background

#calculate max observed for green or red for later filtering
bg_green_high = percentage %>%
    filter(population == "green+", Timepoint == -3) %>%
    pull(true) %>%
    max()

bg_red_high = percentage %>%
    filter(population == "red+", Timepoint == -3) %>%
    pull(true) %>%
    max()

#set background for both green/red based on each mouse
percentage = percentage %>%
    group_by(Mouse, population) %>%
    mutate(bg = true[which(Timepoint == -3)]) %>%
    ungroup() %>%
    mutate(positive_events_minusbg = true - bg)

#set all negative values or 0 values to one
percentage = percentage %>%
    mutate(positive_events_minusbg_nozeros = as.double(positive_events_minusbg)) %>%
    mutate(positive_events_minusbg_nozeros = case_when(positive_events_minusbg_nozeros <= 0 ~ 1, 
                                               positive_events_minusbg_nozeros > 0 ~ positive_events_minusbg_nozeros))

Plot events over time for individual mice

ggplot(percentage) +
    annotate("rect", xmin=0.25, xmax=7.25, ymin=0, ymax=Inf, fill="honeydew2", colour=NA, alpha=0.75) +
    geom_line(aes(x=Timepoint, y=positive_events_minusbg_nozeros, colour=population, group=interaction(Mouse, population)), size=1) +
    geom_point(aes(x=Timepoint, y=positive_events_minusbg_nozeros, colour=population), size=2, alpha=1) +
    scale_y_log10(name="Positive events \n(background subtracted)\n", labels = trans_format("log10", math_format(10^.x))) + 
    scale_x_continuous(name="Day", breaks=seq(-4,15,2)) +
    scale_colour_manual(values=c("green4", "red3")) +
    theme_linedraw(12) +
    theme(panel.grid = element_blank(), panel.grid.major.y = element_blank(), panel.border=element_rect(size=1.5), legend.position = "top") +
    annotation_logticks(sides="l") +
    facet_wrap(~Mouse_Treatment)
## Warning: Transformation introduced infinite values in continuous y-axis

ggsave("figures/exp17_timecourse_green_red_byMouse_unfiltered.png",  height=8, width=12, dpi=150)
## Warning: Transformation introduced infinite values in continuous y-axis
#filter out timepoints where red AND green are both below the highest background observed for each multiplied by a factor
percentage_filtered = percentage %>%
    mutate(bg_high = case_when(population == "green+" ~ bg_green_high, population == "red+" ~ bg_red_high)) %>%  #background depending on fluor
    mutate(below_background = ifelse(true > bg_high * 3, "no", "yes")) %>%                                       #mark whether above or below background
    mutate(Mouse_Timepoint = paste(Mouse, Timepoint))

#make new table of mouse+timepoint to filter by 
filter_below = percentage_filtered %>%
    select(Mouse, Timepoint, Mouse_Timepoint, population, bg, below_background) %>%
    filter(below_background == "yes") %>% 
    filter(Timepoint != -3) %>%
    pivot_wider(names_from=population, values_from=bg) %>%
    filter(is.na(`green+`) == FALSE & is.na(`red+`) == FALSE)

`%notin%` <- Negate(`%in%`)

percentage_filtered = percentage_filtered %>%
    filter(Mouse_Timepoint %notin% filter_below$Mouse_Timepoint)
ggplot(percentage_filtered) +
    annotate("rect", xmin=0.25, xmax=7.25, ymin=0, ymax=Inf, fill="honeydew2", colour=NA, alpha=0.75) +
    geom_point(aes(x=Timepoint, y=positive_events_minusbg_nozeros, colour=population), size=2, alpha=1) +
    geom_line(aes(x=Timepoint, y=positive_events_minusbg_nozeros, colour=population, group=interaction(Mouse, population)), size=1) +
    scale_y_log10(name="Positive events \n(background subtracted)\n", labels = trans_format("log10", math_format(10^.x))) + 
    scale_x_continuous(name="Day", breaks=seq(-4,15,2)) +
    scale_colour_manual(values=c("green4", "red3")) +
    theme_linedraw(12) +
    theme(panel.grid = element_blank(), panel.grid.major.y = element_blank(), panel.border=element_rect(size=1.5), legend.position = "top") +
    annotation_logticks(sides="l") +
    facet_wrap(~Mouse_Treatment)
## Warning: Transformation introduced infinite values in continuous y-axis

ggsave("figures/exp17_timecourse_green_red_byMouse.png", height=8, width=12, dpi=150)
## Warning: Transformation introduced infinite values in continuous y-axis
ggsave("figures/exp17_timecourse_green_red_byMouse.pdf", height=8, width=12)
## Warning: Transformation introduced infinite values in continuous y-axis

Facet events over time by treatment

percentage_filtered = percentage_filtered %>%
    mutate(Treatment_Full = gsub("NT", "Non-Targeting", Treatment)) %>%
    mutate(Treatment_Full = gsub("GFPT", "GFP-Targeting", Treatment_Full)) %>%
    mutate(Treatment_Full = factor(Treatment_Full, levels=c("Non-Targeting", "GFP-Targeting")))

set.seed(111)

#median
ggplot(percentage_filtered) +
    annotate("rect", xmin=0.75, xmax=7.75, ymin=0, ymax=Inf, fill="honeydew2", colour=NA, alpha=0.75) +
    geom_jitter(aes(x=Timepoint, y=positive_events_minusbg_nozeros, colour=population), position=position_jitterdodge(dodge.width=1.1), size=2, alpha=0.5) +
    stat_summary(aes(x=Timepoint, y=positive_events_minusbg_nozeros, group=population, colour=population), size=0.75, alpha=1, 
                 geom="line", position=position_dodge(width=0.5), fun = median) +
    stat_summary(aes(x=Timepoint, y=positive_events_minusbg_nozeros, group=factor(interaction(population, Timepoint)), colour=population), size=0.75, alpha=1, 
                 geom="pointrange", position=position_dodge(width=1), fun = median, fun.min = min, fun.max = max) +
    scale_y_log10(name="Positive events\n(background subtracted)", limits=c(1,40000)) + 
    scale_x_continuous(name="Day", breaks=seq(-4,15,2)) +
    scale_colour_manual(values=c("green4", "red3"), labels=c("GFP+", "mCherry+")) +
    theme_linedraw(14) +
    theme(panel.grid = element_blank(), panel.grid.major.y = element_blank(), panel.border=element_rect(size=1.5), legend.position = "top") +
    annotation_logticks(sides="l") +
    facet_wrap(~Treatment_Full, scales="free_y")
## Warning: Transformation introduced infinite values in continuous y-axis

ggsave("figures/exp17_timecourse_green_red_byTreatment_pointrange_median.png", width=12, height=6, dpi=150)
## Warning: Transformation introduced infinite values in continuous y-axis
ggsave("figures/exp17_timecourse_green_red_byTreatment_pointrange_median.pdf", width=12, height=6)
## Warning: Transformation introduced infinite values in continuous y-axis

Plot percent red / green at Day 14

dayfourteen = percentage_filtered %>%
    filter(Timepoint == 14) 
#calculate percent positive green/red using background subtracted-positive events (no negatives)
dayfourteen = dayfourteen %>%
    mutate(positive_events_minusbg_nonegs = as.double(positive_events_minusbg)) %>%
    mutate(positive_events_minusbg_nonegs = case_when(positive_events_minusbg_nonegs <= 0 ~ 0, 
                                                      positive_events_minusbg_nonegs > 0 ~ positive_events_minusbg_nonegs)) %>%
    group_by(Mouse, Timepoint) %>%
    mutate(positive_events_minusbg_nonegs_gfp_mcherry_total = sum(positive_events_minusbg_nonegs)) %>%
    mutate(positive_events_minusbg_nonegs_percent_fluor = positive_events_minusbg_nonegs / positive_events_minusbg_nonegs_gfp_mcherry_total * 100) %>%
    ungroup()
#order mice by green fluor
green_ranked = dayfourteen %>%
    filter(population=="green+") %>%
    arrange(Treatment, positive_events_minusbg_nonegs_percent_fluor) %>%
    pull(Mouse)

dayfourteen = dayfourteen %>% 
    mutate(Mouse = factor(Mouse, levels=rev(green_ranked))) %>%
    mutate(population = factor(population, levels=c("red+", "green+")))

#make df of labels to plot percentage red value on bar plot
labels = dayfourteen %>%
    filter(population == "green+") %>%
    mutate(labels = paste0(round(positive_events_minusbg_nonegs_percent_fluor, 0), "%")) 
ggplot() +
     geom_bar(data=dayfourteen, aes(x=Mouse, y=positive_events_minusbg_nonegs_percent_fluor, fill=population), stat = "identity") +
     geom_text(data=labels, aes(x=Mouse, y=case_when(positive_events_minusbg_nonegs_percent_fluor > 10 ~ positive_events_minusbg_nonegs_percent_fluor - 4, 
                                                     positive_events_minusbg_nonegs_percent_fluor < 10 ~ positive_events_minusbg_nonegs_percent_fluor + 3),
                                label=labels), colour="white", size=3.5) +
     scale_fill_manual(values = c("red3", "green4"), labels=c("mCherry+", "GFP+")) +
     scale_y_continuous(name="Day 14  Percent GFP+ and mCherry+ events \n(background subtracted)", 
                        expand = c(0, 0), position="right", breaks=c(0,25,50,75,100), labels=c(0,25,50,75,100)) +
     scale_x_discrete(name = "", position = "top") +
     coord_flip() +
     theme_linedraw(14) +
     theme(legend.position = "bottom", legend.title=element_blank(), panel.grid=element_blank(),
           plot.margin=unit(c(5, 5, 5, 15),"mm"), 
           panel.border = element_rect(size=1.25)) +
     guides(fill = guide_legend(nrow = 1))

ggsave("figures/exp17_percent_green_red_barplot.pdf", height=6, width=7)

Calculate significance Day 14

dayfourteen_green = dayfourteen %>%
    filter(population=="green+") %>%
    mutate(Treatment = factor(Treatment, levels=c("NT", "GFPT")))

nt = dayfourteen_green %>% 
    filter(Treatment=="NT") %>%
    pull(positive_events_minusbg_nonegs_percent_fluor)

gfpt = dayfourteen_green %>% 
    filter(Treatment=="GFPT") %>%
    pull(positive_events_minusbg_nonegs_percent_fluor)

(mean_nt = round(mean(nt), 0))
## [1] 81
(mean_gfpt = round(mean(gfpt), 0))
## [1] 6
(median_nt = round(median(nt), 0))
## [1] 99
(median_gfpt = round(median(gfpt), 0))
## [1] 1
#do mann whitney wilcoxon test for nonparametric distribution
(test = wilcox.test(gfpt, nt, alternative = "less"))
## Warning in wilcox.test.default(gfpt, nt, alternative = "less"): cannot compute
## exact p-value with ties
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  gfpt and nt
## W = 2, p-value = 0.0002485
## alternative hypothesis: true location shift is less than 0
pvalue = formatC(test$p.value, format = "e", digits = 2) 
ggplot(dayfourteen_green, aes(x=Treatment, y=percent_fluor, fill=population)) +
    geom_bar(stat="summary", fun = median, colour="black", size=0.75, width=0.5) + 
    geom_jitter(width=0.2, height=0.2, shape=21, colour="black", fill="white", size=4, stroke=1) +
    scale_fill_manual(values=c("green4")) +
    scale_x_discrete(name="") +
    scale_y_continuous(name="Day 14  Percent GFP+ events \n(background subtracted)", expand=c(0,0), limits=c(0, 120), breaks=seq(0,100,20)) +
    annotate("text", x=1.5, y=114, label=paste("p =", pvalue), size=4) +
    annotate("text", x=1.025, y=median_nt+6, label=paste0("median ", median_nt,"%"), size=4, colour="green4") +
    annotate("text", x=2.025, y=median_gfpt+11, label=paste0("median ", median_gfpt,"%"), size=4, colour="green4") +
    geom_segment(x=1, xend=2, y=110, yend=110) +
    theme_linedraw(15) +
    theme(panel.border = element_rect(size=1.5), panel.grid = element_blank(), legend.position="none",
          axis.text.x = element_text(size=16), axis.ticks.x = element_blank())

ggsave("figures/exp17_Day14_green_barplot_pvalue_median.pdf", height=6, width=4)

Log session

sessionInfo()
## R version 3.6.2 (2019-12-12)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Mojave 10.14.6
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] forcats_0.5.1            stringr_1.4.0            dplyr_1.0.5             
##  [4] purrr_0.3.4              readr_1.4.0              tidyr_1.1.3             
##  [7] tibble_3.1.0             tidyverse_1.3.1          Phenoflow_1.1.2         
## [10] foreach_1.5.1            flowAI_1.16.0            flowFDA_0.99            
## [13] mclust_5.4.7             multcomp_1.4-16          TH.data_1.0-10          
## [16] MASS_7.3-53.1            survival_3.2-10          mvtnorm_1.1-1           
## [19] flowFP_1.44.0            flowViz_1.50.0           lattice_0.20-41         
## [22] flowClean_1.24.0         cowplot_1.1.1            scales_1.1.1            
## [25] ggcyto_1.12.0            flowWorkspace_3.32.0     ncdfFlow_2.30.1         
## [28] BH_1.75.0-0              RcppArmadillo_0.10.4.0.0 ggplot2_3.3.3           
## [31] flowCore_1.52.1         
## 
## loaded via a namespace (and not attached):
##   [1] readxl_1.3.1         changepoint_2.2.2    backports_1.2.1     
##   [4] plyr_1.8.6           igraph_1.2.6         splines_3.6.2       
##   [7] digest_0.6.27        htmltools_0.5.1.1    fansi_0.4.2         
##  [10] magrittr_2.0.1       cluster_2.1.1        sfsmisc_1.1-11      
##  [13] recipes_0.1.15       Biostrings_2.52.0    modelr_0.1.8        
##  [16] gower_0.2.2          RcppParallel_5.1.2   matrixStats_0.58.0  
##  [19] sandwich_3.0-0       prettyunits_1.1.1    jpeg_0.1-8.1        
##  [22] colorspace_2.0-0     rvest_1.0.0          rrcov_1.5-5         
##  [25] haven_2.4.0          xfun_0.22            crayon_1.4.1        
##  [28] jsonlite_1.7.2       hexbin_1.28.2        graph_1.62.0        
##  [31] zoo_1.8-9            iterators_1.0.13     ape_5.4-1           
##  [34] glue_1.4.2           gtable_0.3.0         ipred_0.9-11        
##  [37] zlibbioc_1.30.0      XVector_0.24.0       phyloseq_1.28.0     
##  [40] IDPmisc_1.1.20       Rgraphviz_2.28.0     Rhdf5lib_1.6.3      
##  [43] BiocGenerics_0.32.0  DEoptimR_1.0-8       DBI_1.1.1           
##  [46] Rcpp_1.0.6           progress_1.2.2       bit_4.0.4           
##  [49] stats4_3.6.2         lava_1.6.9           prodlim_2019.11.13  
##  [52] httr_1.4.2           RColorBrewer_1.1-2   ellipsis_0.3.1      
##  [55] farver_2.1.0         pkgconfig_2.0.3      nnet_7.3-15         
##  [58] sass_0.3.1           dbplyr_2.1.1         utf8_1.2.1          
##  [61] caret_6.0-86         labeling_0.4.2       tidyselect_1.1.0    
##  [64] rlang_0.4.10         reshape2_1.4.4       cellranger_1.1.0    
##  [67] munsell_0.5.0        tools_3.6.2          cli_2.4.0           
##  [70] generics_0.1.0       ade4_1.7-16          broom_0.7.6         
##  [73] evaluate_0.14        biomformat_1.12.0    yaml_2.2.1          
##  [76] fs_1.5.0             ModelMetrics_1.2.2.2 knitr_1.32          
##  [79] robustbase_0.93-7    nlme_3.1-152         xml2_1.3.2          
##  [82] rstudioapi_0.13      compiler_3.6.2       png_0.1-7           
##  [85] reprex_2.0.0         bslib_0.2.4          pcaPP_1.9-73        
##  [88] stringi_1.5.3        highr_0.8            Matrix_1.3-2        
##  [91] vegan_2.5-7          permute_0.9-5        multtest_2.40.0     
##  [94] vctrs_0.3.7          pillar_1.6.0         lifecycle_1.0.0     
##  [97] jquerylib_0.1.3      data.table_1.14.0    R6_2.5.0            
## [100] latticeExtra_0.6-29  KernSmooth_2.23-18   gridExtra_2.3       
## [103] IRanges_2.18.3       codetools_0.2-18     boot_1.3-27         
## [106] assertthat_0.2.1     rhdf5_2.28.1         withr_2.4.1         
## [109] S4Vectors_0.22.1     mgcv_1.8-34          parallel_3.6.2      
## [112] hms_1.0.0            grid_3.6.2           rpart_4.1-15        
## [115] timeDate_3043.102    class_7.3-18         rmarkdown_2.7       
## [118] pROC_1.17.0.1        Biobase_2.46.0       lubridate_1.7.10